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# Import necessary libraries
import numpy as np
import joblib # For loading the serialized model
import pandas as pd # For data manipulation
from flask import Flask, request, jsonify # For creating the Flask API
# Initialize the Flask application
predictor_api = Flask("SuperKart Price Predictor")
# Load the trained machine learning model
model = joblib.load("superkart_prediction_model_v1_0.joblib")
# Define a route for the home page (GET request)
@predictor_api.get('/')
def home():
"""
This function handles GET requests to the root URL ('/') of the API.
It returns a simple welcome message.
"""
return "Welcome to the Superkart Price Prediction API!"
# Define an endpoint for single property prediction (POST request)
@predictor_api.post('/v1/superkart')
def predict_price():
"""
This function handles POST requests to the '/v1/superkart' endpoint.
It expects a JSON payload containing property details and returns
the predicted sales price as a JSON response.
"""
# Get the JSON data from the request body
property_data = request.get_json()
# Extract relevant features from the JSON data
sample = {
'Product_Weight': property_data['product_weight'],
'Product_Sugar_Content': property_data['product_sugar_content'],
'Product_Allocated_Area': property_data['product_allocated_area'],
'Product_Type': property_data['product_type'],
'Product_MRP': property_data['product_mrp'],
'Store_Size': property_data['store_size'],
'Store_Location_City_Type': property_data['store_location_city_type'],
'Age_Category': property_data['age_category'],
'type_of_food': property_data['type_of_food']
}
# Convert the extracted data into a Pandas DataFrame
input_data = pd.DataFrame([sample])
# Make prediction
predicted_price = model.predict(input_data)[0]
# Convert predicted_price to Python float
predicted_price = round(float(predicted_price), 2)
# The conversion above is needed as we convert the model prediction (log price) to actual price using np.exp, which returns predictions as NumPy float32 values.
# When we send this value directly within a JSON response, Flask's jsonify function encounters a datatype error
# Return the actual price
return jsonify({'Predicted Price': predicted_price})
# Run the Flask application in debug mode if this script is executed directly
if __name__ == '__main__':
predictor_api.run(debug=True)
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